We’re really excited to be supporting Karim and the team at Mendel.ai. The genesis of Mendel came from Karim’s own struggles as a physician to keep up with the latest research and treatments for his patients. His desire to do more for his patients ultimately started him down the path of figuring out the right technology to extend the capabilities of physicians. Mendel is the result of that effort.

Today, Karim and his team are applying new machine learning techniques to process several challenging datasets – a patient’s medical records & genomic data and the corpus of 24M+ unstructured publications for research/trials/treatments. Via Mendel’s product, physicians will soon have additional insights available to them as they make decisions on the best way to deliver care to their patients.

This approach fits squarely within my vertical data company investment thesis, in that they are leveraging proprietary data, cheap compute resources, and machine learning to solve a problem in a vertical where the founder has deep domain expertise. They join fellow portfolio companies Ginger.io, Ovia Health, and Impact Health in that they are applying this model to fix how we deliver health care.

Here’s the content from the TechCrunch article about Mendel from Sarah Buhr (7/1):

Dr. Karim Galil was tired. He was tired of losing patients to cancer. He was tired of messy medical records. And he was tired of trying to stay on top of the avalanche of clinical trials touting one solution or another. Losing both patience and too many patients, Galil decided to create an organized and artificially intelligent system to match those under his care with the best diagnostic and treatment methods available.

He called his new system Mendel.ai after Gregor Mendel, the father of modern genetics science, and has just raised $2 million in seed funding from DCM Ventures, Bootstrap Labs and Launch Capital to get the project off the ground.

Mendel.ai is similar in many ways to the U.K.-based BenevolentBio, which is focused on skimming through scientific papers to find the latest in cutting-edge medical research. But rather than using keyword data, Mendal.ai uses an algorithm that understands the unstructured, natural language content within medical documents pulled from clinicaltrials.gov, and then compares it to a patient’s medical record. The search process returns a fully personalized match and evaluates the patient’s eligibility for each suggested treatment within minutes, according to Galil.

The startup could prove useful for doctors who increasingly find it difficult to keep up on the exhaustive amount of clinical data.

Patients are also overwhelmed at the prospect of combing through mountains of clinical trial research. “A lung cancer patient, for example, might find 500 potential trials on clinicaltrials.gov, each of which has a unique, exhaustive list of eligibility criteria that must be read and assessed,” says Galil. “As this pool of trials changes each week, it is humanly impossible to keep track of all good matches.”

Mendel.ai seeks to reduce the time it takes and thus save more lives. The company is now integrating with the Comprehensive Blood & Cancer Center (CBCC) in Bakersfield, Calif, which will allow the center’s doctors to quickly match their patients with available clinical trials in a matter of minutes, according to Galil.

The plan going forward is to work with hospitals and cancer genomics companies like the CBCC to improve Mendel.ai and introduce the system. A more immediate goal, Galil says, would be challenging IBM’s Watson against his system to see which one can match up the patients better.

“This is the difference between someone dying and someone living. It’s not a joke,” Galil told TechCrunch.